I’m here showing how you can use GDAL2Tiles to generate map tiles of Tom Patterson’s Natural Earth II. This is a beautiful raster map that portrays the world environment in an idealised manner with little human influence. The map can be downloaded on this page. I’m here using version 2C.
I start by using gdal_translate to georeference the map image:
The image should now have the correct projection (EPSG:4326), but GDAL2Tiles was not generating the KML SuperOverlay before ran this command:
The original raster map is 16,200 x 8,100 pixels. Each map tile image is 256 x 256 pixels. This table shows the number of tiles generated for each zoom level:
For my purpose, 5 zoom levels are enough. I therefore reduced the map image to 8192 x 4096 pixels:
Finally, I used GDAL2Tiles to generate the map tiles:
See the result as a KML SuperOverlay in Google Earth or in Open Layers. There seems to be a problem that OpenLayers can’t georeference TMS map tiles. It is therefore difficult to combine a map tile layer with other map layers.
UPDATE 2 APRIL 2008: The OpenLayers example above is now working properly thanks toChristopher Schmidt.
http://www.gdal.org/gdal_translate.html---可以查看gdal的帮助文档。查看那个命令的帮助直接替换了gdal_translate就可以了
另一种方法：
Step 1: Download and install GDAL
Begin by downloading and installing the GDAL as detailed here.
Step 2: Download an image
You can use any image. There are a number of sources of geographic data on the web. You can use any of them, but you should know the boundaries of the image—the latitude and longitude of each of the corners of the image. This tutorial uses a NASA Blue Marble image, available for download from NASA's website. These images were taken in 2004 and present a beautiful image of the Earth from space. Choose one of the files in the lower right of the right navigation bar.
If you're using your own image and know that it is already georectified, then you can skip to Step 5. Otherwise, proceed with Step 3.
Step 3: Get information about the image
Once you've installed the GDAL libraries and selected the image, you need to get some information about the image so that you can georeference it. Specifically, you need the pixel and line positions of each corner of the image. If you imagine the image as a table, with columns and rows, the pixels are the columns, and the lines are rows.
GDAL provides a handy utility, gdalinfo, for capturing this information. At the command line, simply typegdalinfo filename, replacing filename with the path to the file. You should get output that looks like this:
Driver: JPEG/JPEG JFIF
Files: world_200401.jpg
Size is 21600, 10800
Coordinate System is `'
Image Structure Metadata:
SOURCE_COLOR_SPACE=YCbCr
INTERLEAVE=PIXEL
COMPRESSION=JPEG
Corner Coordinates:
Upper Left ( 0.0, 0.0)
Lower Left ( 0.0,10800.0)
Upper Right (21600.0, 0.0)
Lower Right (21600.0,10800.0)
Center (10800.0, 5400.0)
Band 1 Block=21600x1 Type=Byte, ColorInterp=Red
Image Structure Metadata:
COMPRESSION=JPEG
Band 2 Block=21600x1 Type=Byte, ColorInterp=Green
Image Structure Metadata:
COMPRESSION=JPEG
Band 3 Block=21600x1 Type=Byte, ColorInterp=Blue
Image Structure Metadata:
COMPRESSION=JPEG
The important information for this tutorial is the Upper Left, Lower Left, Upper Right, Lower Right lines. These tell you the pixel and line values of each corner. The Upper Left, in this case, is at 0,0, and the Lower Right is at 21600,10800.
Step 4: Georeference the Image
Georeferencing in this case means to create metadata describing the geographic position of each of the corners of the image. Using the information gained in Step 3 and gdal_translate, you can assign georeference information to the file. This creates a VRT file from world_200401.jpg image, bluemarble1.vrt. VRT files are XML files that contain the information about a particular transformation, in this case the gdal_translate step. You will use it again in the next step to create your final set of tiles. gdal_translate allows you to do multiple file output types including major image file formats. Using VRT outputs allows you to essentially put off making output files until the last step. This increases efficiency and decreases your wait time for individual steps if you're doing the command line. Here's the command you would run:
gdal_translate -of VRT -a_srs EPSG:4326 -gcp 0 0 -180 90 -gcp 21600 0 180 90 -gcp 21600 10800 180 -90 world_200401.jpg bluemarble1.vrtThere's a lot of information on that line, so here it is broken out:
-ofis output format, in this case VRT.-a_srsassigns a spatial reference system to the file. That tells any application consuming it what coordinate system is being used. In this case, it is using EPSG:4326, which is the same as WGS84, the coordinate system used by Google Earth.-gcp, or ground control point, assigns coordinates to positions in the file. In this case, you actually only need three points, since the image is a rectangle and therefore the fourth point can be easily identified. For-gcp, define the gcp by setting the pixel and then line number, and then the longitude and latitude. Each of those is separated by a space.- The last two parameters are the origin file and the target file.
Step 5: Warp the Image
The original image wasn't created for a round globe, it was created to appear to lie flat. In GIS terms, it is projected, which means that it is a two-dimensional representation of a three-dimensional object. Projection requires distorting the image so that it appears how you would expect a flat image of the Earth to look.
In order to get it to look right, you have to warp the image it to fit the globe. Fortunately GDAL provides a great tool for that too. Simply type gdalwarp -of VRT -t_srs EPSG:4326 bluemarble1.vrt bluemarble2.vrt. This will create a new file, bluemarble2.vrt, which provides metadata about the warping procedure.
Step 6: Create the Tiles
You're almost done, but this part will take the longest. To create the tiles, type in gdal2tiles.py -p geodetic -k bluemarble2.vrt. The -k forces a KML output. This will create a directory structure with a super-overlay. As each of those image files has to be created separately, it takes awhile to run. For large images, you can now go, get a cup of coffee, take a nap, maybe get a light meal. When you're done, open up doc.kml and observe the results!
也可以使用gdal_retile.py形成切片 发不到geoserver上
gdal_retile.py -levels 3 -ps 2048 2048 -co "TILED=YES" -co "BLOCKXSIZE=256" -co "BLOCKYSIZE=256" -s_srs EPSG:4326 -targetDir bmpyramid bluemarble2.vrt
Conclusion
This tutorial just scratches the surface of what GDAL can do, but it does provide a convenient mechanism for generating tiles. The core GDAL libraries are written in C++, but they provide bindings for Perl, Python, VB6, R, Ruby, Java, and C#/.NET, meaning you can easily incorporate GDAL into your own applications. Also, many of the utilities, including gdal2tiles, are written in Python, making them easy to incorporate into Python applications.gdal2tiles also has the ability to generate Google Maps API and OpenLayers pages.
欢迎大家来我的新家看一看 3wwang个人博客-记录走过的技术之路